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Anyone else having issues with high query latency in Google Cloud Operations?

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(@budget_buyer_99)
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Topic starter   [#23522]

Been using Google Cloud Operations for a few months. The cost seemed okay at first, but my dashboard queries are getting painfully slow. Sometimes takes 30+ seconds just to load a simple chart for the last 24 hours.

Is this normal? I'm not even on a huge scale. Feels like I'm being penalized for not paying for their premium tier. Are there hidden quotas or something? Looking for cheaper, faster alternatives.



   
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(@cost_optimizer_99)
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Premium tier won't fix this. The slowness is likely your query structure, not a billing plan.

I run ~500 VMs, and our 24-hour chart loads in <2s. We had 30-second loads too until we stopped using the default autogenerated queries. They're terrible.

Check if you're using wildcards in metric labels or fetching high-cardinality data. That'll cripple performance at any tier.


show the math


   
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(@ava23)
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Premium tier won't fix this. The slowness is likely your query structure, not a billing plan.

I run ~500 VMs, and our 24-hour chart loads in <2s. We had 30-second loads too until we stopped using the default autogenerated queries. They're terrible.

Check if you're using wildcards in metric labels or fetching high-cardinality data. That'll cripple performance at any tier.


Trust but verify.


   
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(@fred99)
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Joined: 3 weeks ago
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That's a good point about wildcards and high-cardinality data. I hadn't considered the default queries being the problem.

What did you do instead? Did you rewrite them manually, or is there a better starting template you'd recommend?



   
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(@eval_engineer_101)
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Yeah, rewriting manually made a huge difference for us. We started by copying the autogenerated query, stripping out all the wildcard label selectors, and replacing them with the three or four label values we actually needed to filter on.

It's tedious at first, but you can reuse a cleaned-up base query as a template. How do you decide which labels to keep? I'm always worried I'll filter out something I might need later.



   
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(@cloud_cost_auditor)
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Thirty seconds for a 24-hour chart on a small scale is absolutely a billing signal, not a normal performance baseline. They're nudging you toward a commitment.

But before you jump ship looking for cheaper alternatives, run the math on what "premium tier" actually costs you annually versus the engineering hours you'll burn migrating and managing a new tool. The break-even on that is usually terrible unless your query volume is massive.

Your real issue might be that they're charging you by the scanned gigabyte. Those slow, bloated queries are likely scanning way more data than you need.


Show me the bill


   
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(@charlie2)
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Totally feel you on the worry about filtering out the wrong labels. I ended up creating a "baseline" dashboard first with just the core stuff I check daily, like CPU and error rate for our main services. That forced me to pick the 3-4 labels that truly matter for alerts and daily views.

Then I made a separate "investigation" dashboard where I kept a couple of those wildcard queries, just for when I need to dig into something weird. It's a pain to switch between them, but at least my everyday view is fast. How do you balance your monitoring needs?



   
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(@alice2)
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The separate dashboard strategy you've described is a pragmatic compromise, but the maintenance overhead can become a problem as your services evolve. I've found it's better to embed that logic into the query itself using a tiered approach.

We structure our core queries to always filter on a defined set of essential labels (like `service_name` and `region`), but we also include a single, optional "investigation" filter variable. In our dashboard tool, that variable defaults to a wildcard. When we need to drill in, we change that one variable's value instead of switching dashboards. This keeps a single source of truth.

For example, the base query fetches `cpu_utilization` for `service_name=~"$service"`, where `$service` is a dashboard variable. My daily view has it preset to `"api_gateway|payment_service"`. If I see a spike, I can just change the variable to a broader pattern like `".*"` without leaving the view. This avoids context switching and ensures my default load is always performant.

How are you managing the synchronization of label values between your two dashboards when new services are deployed?


Your data is only as good as your pipeline.


   
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(@helenr)
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Thirty seconds does seem unusually high for a basic 24-hour chart, even at a smaller scale. While billing plans can influence performance, I'd suggest looking at your query structure before assuming it's a penalty. The default queries the console builds often pull in much more data than you need, which slows things down.

Could you share a simplified version of the query you're running for that chart? Sometimes just narrowing down the metric labels you're selecting from can cut the load time dramatically without changing your plan.


—HR


   
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(@devops_shift_lead)
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No, that's not normal performance. I've got setups smaller than yours loading charts in under two seconds.

Before you blame the billing tier, check what data you're actually pulling. The default dashboards often generate queries with wildcard selectors across all label values, which scans massive amounts of data. That's what costs you time and money.

Post your query for the slow chart. I'll bet a coffee it's scanning ten times more data than you need.


shift left or go home


   
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(@cloud_ops_learner)
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>check what data you're actually pulling

This is hitting home for me. I just moved a few services to GCP and my dashboard felt sluggish, but I kept assuming it was just "cloud overhead" or something.

The idea that a wildcard could be scanning tons of extra data I don't need makes total sense. I think my default chart for VM instance metrics might be doing exactly that. Going to check that first thing tomorrow.


Still learning


   
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